Some of the most prominent supply chain challenges within the automotive industry are:
· Increased marginal erosion and procurement challenges as an outcome of high logistics and inventory costs, higher risk of product obsolescence, insufficient warehouse management, and part counterfeiting.
· Lack of sustainable business models affects decision-making processes, impedes end-to-end supply chain visibility, and impairs forecasting capabilities.
· Inadequate data management, insufficient Key Performance Indicators (KPIs) for benchmarking, and poor demand forecasting as an outcome of a lack of data on stock turn, sell-through, historical sales, promotions, and seasonal variation.
· Legacy and disconnected systems that operate in silos, resulting in poor inventory tracking, incorrect orders, and a lack of transparency between departments
· Small planning teams managing large amounts of data, limiting their ability to make the best planning decisions
If one were to closely, all these challenges center around one aspect, ‘Data management’. Hence, the solution to these challenges lies within ‘Intelligent use of data’. As our economic model transitions to a modern economy giving rise to the megatrend of a data-led economy with vast amounts of data entering the system on a continuous basis, data could be the solution to the challenges faced by automotive aftermarket suppliers. Data can be a powerful asset when used efficiently and smartly, and it reflects our government’s macroeconomic efforts to transition to a data-driven economy. The supply chain deals with hosts of stakeholders working across different levels and producing a high amount of data. Hence, there is a strong need for data-led insights and analytics within automotive supply chain management.
It is now up to industry players, such as automotive suppliers, to embrace the megatrend of data-driven business frameworks to address their challenges and create smarter supply chains. Technologies like artificial intelligence, machine learning, blockchain and cloud computing and IoT can serve as a major enabler here, providing a platform to integrate all technologies like predictive analysis, data analytics and enable intelligent use of data.
Data incorporation strategies: Extracting and harnessing data
We clearly have mountains of data, with more arriving every day, but the real question is how automotive players can use this data to their advantage. More than data, reliance is placed on technologies that can assist in data collection, extraction, segregation, and utilization. For example, digitization has changed the way customers interact with automotive players – how they research, purchase, and maintain their vehicles. Hence, suppliers must improve their customer connectivity, demand forecasting, market analysis, and other measures, all while studying the massive amount of data coming in from multiple channels.
This data comes from both internal and external sources, such as social media channels used by brands to connect with their customers and is frequently dispersed across organizational silos. While data may greatly assist aftermarket suppliers in accurately forecasting customer demands across the supply chain, there is a greater need for technology that organizes all this data onto a single platform, providing a much more holistic view. For example, by extracting and harnessing data via smart technologies such as Cloud Analytics and Artificial Intelligence, aftermarket suppliers can study customer behavior analytics while also collaborating it across the supply chain. By obtaining baseline statistical forecasts of all automotive parts based on average sales, planners can make better decisions for their products. KPIs and reporting help to gain a better understanding of profitability drivers and aid in the analysis of historical demand trends.
While many supply chain management technologies, such as tracking and management solutions and customer service solutions, have become mainstream, there is a need to employ technologies that deal with multiple databases and frameworks to fully harness the potential of data. Automotive players can generate anticipatory analysis to future-proof their supply chain by harnessing data through technologies such as cloud analytics, IoT analytics, and blockchain. As part of anticipatory analysis – Point of sale (POS) data, inventory data, and production volumes can all be analyzed in real time to identify supply and demand mismatches. These can then be used to drive actions such as price changes, promotion timing, or the addition of new lines to realign things.
In a nutshell, data is only as effective as the measures used to integrate it into the business framework and automotive supply chains. By combining logistics and supply chain management with data and tracking, integrated decision-making, technological innovation, and strong logistical partnerships, companies and automotive suppliers may expect to be “more in control” in the future.
The author is Senior Director, Cloud ERP, Oracle India.